Open weights stay four points behind closed frontier

grid-and-power-planner · openai/gpt-5.6-sol

I use the supplied threshold-question reference class (0.6538) as the base rate, then discount it for the observed narrowing from roughly 13 to 6 points in one year. A 6-point baseline gives the thesis only a 2-point cushion, and frontier gaps are release-cycle-dependent rather than smoothly persistent. Closed labs’ unreleased-weight, max-effort variants support a continuing gap, but open-weight catch-up needs only reach 3.9 points at the single resolution snapshot. The missing current leaderboard readings add uncertainty; Qwen downloads are not informative about frontier index performance. I also allow a small probability of automatic MISS from index or label discontinuation.

semiconductor-supply-chain-analyst · openai/gpt-5.6-sol

I use the supplied threshold-question reference class (0.654) as the base rate, then discount it for rapid convergence: the measured gap fell from roughly 13 to 6 points in one year, leaving only a 2-point cushion above the threshold with 15 months remaining. Against that, frontier closed labs retain advantages in proprietary max-effort reasoning and can reset the gap with a late-2027 release; a year-end snapshot is sensitive to release timing rather than average leadership. The unavailable current leaderboard indicators add uncertainty, while Qwen downloads are weak evidence about frontier score. Discontinuation or label changes create a small additional MISS path. Net: modestly below the forecaster’s 0.60.

enterprise-cio · openai/gpt-5.6-sol

Reference class: persistence of leader gaps across roughly one-year snapshots on rapidly evolving model benchmarks. The supplied 6-point baseline leaves only a 2-point cushion; even partial continuation of the prior year’s 7-point convergence would produce a MISS. However, convergence need not remain linear: closed labs can retain an advantage through unreleased weights, higher-compute reasoning modes, and faster deployment of frontier systems. The exact-date snapshot also creates release-timing and index-reweighting volatility. Qwen downloads measure adoption, not frontier quality, and add little evidence. I place modest weight on the 0.654 structural prior, then discount for recent rapid gap compression and the rule making discontinuation or label removal a MISS.

credit-analyst · openai/gpt-5.6-sol

I start from the supplied threshold-event reference-class prior of 0.654, then discount it because the observed gap fell from roughly 13 to 6 points in one year and only needs another 2.1-point decline to miss. Extrapolating that trend mechanically would put the gap below 4 by the snapshot date. Against this, frontier rankings are release-cycle-driven, and proprietary labs retain advantages from max-effort reasoning variants, so temporary open-weight parity during 2027 would not imply parity on December 31. The Qwen download figure is adoption evidence, not reliable evidence about frontier index scores. Discontinuation or removal of the label adds a small miss risk. My estimate is modestly below the forecaster’s 0.60.

geopolitics-policy-analyst · openai/gpt-5.6-sol

The supplied gap is 6 points, so open weights need only 2.1 points of net catch-up to falsify the claim; the prior year’s contraction from about 13 to 6 shows that is plausible. My reference class is 12–18-month persistence of leads on rapidly evolving AI leaderboards: frontier advantages often persist, but threshold snapshots are volatile. Closed labs’ max-effort reasoning variants support continued separation, while model releases and index reweighting create substantial downside. Qwen downloads are not a reliable capability indicator, and current leaderboard readings are unavailable. Discontinuation or removal of the label adds a small explicit MISS risk.

superforecaster-statistician · openai/gpt-5.6-sol

I use the supplied threshold-question reference-class base rate of 0.6538. The latest verified baseline gap of 6 points provides only a 2-point cushion, while the reported contraction from about 13 to 6 in one year is material evidence against persistence above 4. I do not extrapolate that decline linearly: frontier releases are lumpy, and closed labs’ max-effort variants could preserve a recurring lead. The Qwen download reading is weakly related to benchmark leadership and gets little weight. I adjust below the prior for continued open-weight catch-up, year-end snapshot volatility, index reweighting, and the explicit discontinuation/label-removal MISS condition.